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Developing an Ensemble Predictive Safety Risk Assessment Model: Case of Malaysian Construction Projects

Author

Listed:
  • Haleh Sadeghi

    (Department of Civil and Environmental Engineering, The Hong Kong University of Science and Technology, Clear Water Bay, Hong Kong, China)

  • Saeed Reza Mohandes

    (Department of Civil and Environmental Engineering, The Hong Kong University of Science and Technology, Clear Water Bay, Hong Kong, China)

  • M. Reza Hosseini

    (School of Architecture and Built Environment, Deakin University, Geelong 3217, VIC, Australia)

  • Saeed Banihashemi

    (Department of Building and Construction Management, University of Canberra, Bruce 2617, ACT, Australia)

  • Amir Mahdiyar

    (School of Housing, Building and Planning, Universiti Sains Malaysia, Penang 11800, Malaysia)

  • Arham Abdullah

    (Universiti Malaysia Kelantan, Beg Bercunci No. 01, Bachok, Kelantan 16300, Malaysia)

Abstract

Occupational Health and Safety (OHS)-related injuries are vexing problems for construction projects in developing countries, mostly due to poor managerial-, governmental-, and technical safety-related issues. Though some studies have been conducted on OHS-associated issues in developing countries, research on this topic remains scarce. A review of the literature shows that presenting a predictive assessment framework through machine learning techniques can add much to the field. As for Malaysia, despite the ongoing growth of the construction sector, there has not been any study focused on OHS assessment of workers involved in construction activities. To fill these gaps, an Ensemble Predictive Safety Risk Assessment Model (EPSRAM) is developed in this paper as an effective tool to assess the OHS risks related to workers on construction sites. The developed EPSRAM is based on the integration of neural networks with fuzzy inference systems. To show the effectiveness of the EPSRAM developed, it is applied to several Malaysian construction case projects. This paper contributes to the field in several ways, through: (1) identifying major potential safety risks, (2) determining crucial factors that affect the safety assessment for construction workers, (3) predicting the magnitude of identified safety risks accurately, and (4) predicting the evaluation strategies applicable to the identified risks. It is demonstrated how EPSRAM can provide safety professionals and inspectors concerned with well-being of workers with valuable information, leading to improving the working environment of construction crew members.

Suggested Citation

  • Haleh Sadeghi & Saeed Reza Mohandes & M. Reza Hosseini & Saeed Banihashemi & Amir Mahdiyar & Arham Abdullah, 2020. "Developing an Ensemble Predictive Safety Risk Assessment Model: Case of Malaysian Construction Projects," IJERPH, MDPI, vol. 17(22), pages 1-25, November.
    • Handle: RePEc:gam:jijerp:v:17:y:2020:i:22:p:8395-:d:444340
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    References listed on IDEAS

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    1. Xiang Wu & Yuanlong Li & Yongzheng Yao & Xiaowei Luo & Xuhui He & Wenwen Yin, 2018. "Development of Construction Workers Job Stress Scale to Study and the Relationship between Job Stress and Safety Behavior: An Empirical Study in Beijing," IJERPH, MDPI, vol. 15(11), pages 1-12, October.
    2. Antoine J.‐P. Tixier & Matthew R. Hallowell & Balaji Rajagopalan, 2017. "Construction Safety Risk Modeling and Simulation," Risk Analysis, John Wiley & Sons, vol. 37(10), pages 1917-1935, October.
    3. Behzad Esmaeili & Matthew Hallowell, 2013. "Integration of safety risk data with highway construction schedules," Construction Management and Economics, Taylor & Francis Journals, vol. 31(6), pages 528-541, June.
    4. Banus Kam Leung Low & Siu Shing Man & Alan Hoi Shou Chan, 2018. "The Risk-Taking Propensity of Construction Workers—An Application of Quasi-Expert Interview," IJERPH, MDPI, vol. 15(10), pages 1-11, October.
    5. Chaohua Xiong & Kongzheng Liang & HanBin Luo & Ivan W. H. Fung, 2018. "Identification of Safety-Related Opinion Leaders among Construction Workers: Evidence from Scaffolders of Metro Construction in Wuhan, China," IJERPH, MDPI, vol. 15(10), pages 1-17, October.
    6. Hye-Suk Yi & Sangyoung Park & Kwang-Guk An & Keun-Chang Kwak, 2018. "Algal Bloom Prediction Using Extreme Learning Machine Models at Artificial Weirs in the Nakdong River, Korea," IJERPH, MDPI, vol. 15(10), pages 1-20, September.
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    Cited by:

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    2. Mohanad Kamil Buniya & Idris Othman & Serdar Durdyev & Riza Yosia Sunindijo & Syuhaida Ismail & Ahmed Farouk Kineber, 2021. "Safety Program Elements in the Construction Industry: The Case of Iraq," IJERPH, MDPI, vol. 18(2), pages 1-13, January.
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    4. Antonella Pireddu & Angelico Bedini & Mara Lombardi & Angelo L. C. Ciribini & Davide Berardi, 2024. "A Review of Data Mining Strategies by Data Type, with a Focus on Construction Processes and Health and Safety Management," IJERPH, MDPI, vol. 21(7), pages 1-26, June.

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